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This book presents a type of hierarchical optimization models, stochastic equilibrium problems with equilibrium constraints, which have wide applicability across diverse domains in engineering design, management and energy economics. The book represents the PhD research experience of the author''s involvement in the methodology and application of stochastic equilibrium problems with equilibrium constraints. To computationally cope with the randomness in the problem, the book also draws together recent development on Monte Carlo sampling methods. In addition, the book includes several chapters to illustrate how to implement the methodology results to modeling issues in energy markets. The author hopes that this book will be interesting to researchers, keen to hierarchical game theory, and can help them to find a statistical method to cope with the uncertainty in real-world problems. The author also hopes that the book will be interesting to policy analysts and makers in energy markets, and can provide them some fundamental knowledge on how game theory can be used to characterize the interactions among different entities in an electricity market.